CFP last date
20 June 2024
Reseach Article

Fuzzy C- Means Clustering with Kernel Metric and Local Information for Image Segmentation

Published on June 2015 by Pallavi Khare, Anagha Gaikwad, Pooja Kumari
National Conference on Emerging Trends in Advanced Communication Technologies
Foundation of Computer Science USA
NCETACT2015 - Number 5
June 2015
Authors: Pallavi Khare, Anagha Gaikwad, Pooja Kumari
c9eeb8b9-9e52-4fbf-9a05-bc09d8073f3c

Pallavi Khare, Anagha Gaikwad, Pooja Kumari . Fuzzy C- Means Clustering with Kernel Metric and Local Information for Image Segmentation. National Conference on Emerging Trends in Advanced Communication Technologies. NCETACT2015, 5 (June 2015), 19-23.

@article{
author = { Pallavi Khare, Anagha Gaikwad, Pooja Kumari },
title = { Fuzzy C- Means Clustering with Kernel Metric and Local Information for Image Segmentation },
journal = { National Conference on Emerging Trends in Advanced Communication Technologies },
issue_date = { June 2015 },
volume = { NCETACT2015 },
number = { 5 },
month = { June },
year = { 2015 },
issn = 0975-8887,
pages = { 19-23 },
numpages = 5,
url = { /proceedings/ncetact2015/number5/21013-2063/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Emerging Trends in Advanced Communication Technologies
%A Pallavi Khare
%A Anagha Gaikwad
%A Pooja Kumari
%T Fuzzy C- Means Clustering with Kernel Metric and Local Information for Image Segmentation
%J National Conference on Emerging Trends in Advanced Communication Technologies
%@ 0975-8887
%V NCETACT2015
%N 5
%P 19-23
%D 2015
%I International Journal of Computer Applications
Abstract

Image segmentation has been an intriguing area for research and developing efficient algorithms, playing a paramount role in high caliber image interpretation and image analysis. Segmentation of images plays an imperative role in medical diagnosis. Such segmentation demands a robust segmentation algorithm against noise. The legendary orthodox fuzzy c-means algorithm is proficiently exploited for clustering in medical image segmentation. FCM is highly sensitive to noise due to the practice of only intensity values for clustering. Thus this paper aims to apply the 'kernel method', instituted on the conventional fuzzy clustering algorithm (FCM) to swap the Euclidean metric norm to a novel kernel-induced metric in the data space. Images can be segmented by pixel classification through clustering of all features of interest. In unsupervised methods of clustering algorithms utilizing kernel method, a nonlinear mapping is operated initially in order to map the data into a much higher space feature, and then clustering is executed. The integer of clusters in the multidimensional feature space thus represents the number of classes in the image. As the image is sorted into cluster classes, segmented regions are obtained by examination of the neighborhood pixels for the same class label. Since clustering produces disjointed regions with holes or regions with a single pixel, a post processing algorithm such as region growing, pixel connectivity, or a rule-based algorithm is applied to obtain the final segmented regions.

References
  1. Fast and Robust Fuzzy C-Means Clustering Algorithms Incorporating Local Information for Image Segmentation by Weiling Cai, Songcan Chen and Daoqiang Zhang.
  2. S. Krinidis and V. Chatzis, "A robust fuzzy local information C-means clustering algorithm," IEEE Trans. Image Process. , vol. 19, no. 5, pp. 1328–1337, May 2010.
  3. P. Sivasangareswari, K. Sathish Kumar, "Fuzzy C-Means Clustering With Local Information and Kernel Metric For Image Segmentation", International Journal of Advanced Research in Computer Science & Technology, ISSN - 2347 –8446, 2014.
  4. Ajala Funmilola A, Oke O. A, Adedeji T. O, Alade O. M, Adewusi E. A, "Fuzzy k-c means Clustering Algorithm for Medical Image Segmentation", Journal of Information Engineering and Applications, Vol. 2, ISSN - 2225-0506, 2012.
  5. J. C. Bezdek ,Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press ,New York,1981
  6. S. Chen and D. Zhang, "Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure," IEEE Trans. Syst. , Man, Cybern. , B, Cybern. , vol. 34, no. 4, pp. 1907–1916, Aug. 2004.
  7. Y. Tolias and S. Panas, "Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions," IEEE Transactions on Systems, Man and Cybernetics, vol. 28, no. 3, pp. 359–369, March 1998.
  8. L. Szilagyi ,Z. Benyo, S. Szilagyii, and H. Adam, "MR brain image segmentation using an enhanced fuzzy C-means algorithm," in proce. 25th Annu. I nt. Conf. IEEE EMBS, Nov. 2003, pp. 17-21.
  9. F. Masulli and A. Schenone, "A fuzzy clustering based segmentation system as support to diagnosis in medical imaging," med, vol. 16, no 2, pp. 129-147, 1999. Fu, S. K. —Mui, J. K. : A Survey on Image Segmentation. Pattern Recognition, Vol. 13, 1981, pp. 3–16.
  10. Hung M, D. ang D, 2001 "An efficient fuzzy c-means clustering algorithm". In Proc. the 2001 IEEE International Conference on Data Mining.
  11. "Medical image analysis" , Atam P. Dhawan, Second edition, Published by John Wiley & Sons, Inc. , Hoboken, New Jersey
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Clustering Kernel Nonlinear Fcm